大規模ヒューマンデータを用いた自動運転レースのシミュレーションベンチマーク
A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data
Assetto Corsaをベースに自動運転レースアルゴリズムを評価するシミュレーションプラットフォームを構築し、強化学習やMPCのベンチマークと人間ドライバーのデータセットを公開した。
著者: Adrian Remonda, Nicklas Hansen, Ayoub Raji, Nicola Musiu, Marko Bertogna, Eduardo Veas, Xiaolong Wang
分類: cs.RO, cs.LG
原文アブストラクト
Despite the availability of international prize-money competitions, scaled vehicles, and simulation environments, research on autonomous racing and the control of sports cars operating close to the limit of handling has been limited by the high costs of vehicle acquisition and management, as well as the limited physics accuracy of open-source simulators. In this paper, we propose a racing simulation platform based on the simulator Assetto Corsa to test, validate, and benchmark autonomous driving algorithms, including reinforcement learning (RL) and classical Model Predictive Control (MPC), in realistic and challenging scenarios. Our contributions include the development of this simulation platform, several state-of-the-art algorithms tailored to the racing environment, and a comprehensive dataset collected from human drivers. Additionally, we evaluate algorithms in the offline RL setting. All the necessary code (including environment and benchmarks), working examples, datasets, and videos are publicly released and can be found at: https://assetto-corsa-gym.github.io
